Differential stability of task variable representations in retrosplenial cortex

Cortical neurons store information across different timescales, from seconds to years. Although information stability is variable across regions, it can vary within a region as well. Association areas are known to multiplex behaviorally relevant variables, but the stability of their representations is not well understood. Here, we longitudinally recorded the activity of neuronal populations in the mouse retrosplenial cortex (RSC) during the performance of a context-choice association task. We found that the activity of neurons exhibits different levels of stability across days. Using linear classifiers, we quantified the stability of three task-relevant variables. We find that RSC representations of context and trial outcome display higher stability than motor choice, both at the single cell and population levels. Together, our findings show an important characteristic of association areas, where diverse streams of information are stored with varying levels of stability, which may balance representational reliability and flexibility according to behavioral demands.

e. Fraction of neurons with activity levels within 1 standard deviation of the difference between correct and incorrect (C -I) in gray, and higher that 1 standard deviation in correct (black) and incorrect (red) responses.
For all panels: s = trial start; d = decision point; e = trial end.
b. Averaged normalized spikes in the population of neurons separated by their preferred context.Only comparisons between days 1 and 2, and days 1 and 4 are shown for clarity.Spikes are cross-validated, sorted by latency using odd trials in day 1, and plotted using even trials for days 2 or 4. c.Heat maps of context (left), motor choice (middle-left), post-decision outcome (middle-right), and post-trial outcome (right) coding performance along trial duration and across all 5 days of experimentation.Note the similar dynamics in the encoding and stability of task variables across days (Fig. 7b), even when estimated from inferred spikes.For all panels: s = trial start; d = decision point; e = trial end.

e.
Decision delay in correct (n = 140-448 trials per day) and incorrect (n = 31-198 trials per day) decisions across all 6 mice during training.Solid line, mean; shaded area, s.e.m.Mice tended to make faster decisions in correct trials by the end of training.f.Histogram showing the decision delay for correct (n = 1,257) and incorrect (n = 428) trials during imaging sessions across all 6 mice.The median is indicated by a solid line.Mice maintained their faster decisions for correct trials during imaging sessions.g.Joystick trajectories during decisions in correct (n = 3,735 trials) and incorrect trials (n = 1,225 trials) across all 6 mice.The angle rotation is color-coded as indicated.Supplementary Fig. 5. Similar statistics in neural activity on independent days.Related to Fig. 2. a.Average normalized activity in correct trials for both contexts across the 5 days of experimentation.Responses are cross-validated, sorted in odd trials, and plotted in even trials for each day independently.Note a similar mapping along trial duration by each subpopulation of cells determined in each day independently.b.Fraction of neurons with peak activity along trial duration for context 1 (yellow) and for context 2 (blue).To estimate population error, the population was randomly sampled (n = 100 neurons per iteration), and the mean ± bootstrapped s.e.m. of the response fraction was plotted.c.Histograms of the preference index for the cells in Supplementary Fig. 5a.Note a similar preference for either context in each population of cells.d.Average activity in individual neurons for correct and incorrect responses along trial duration.To estimate population error, the population was randomly sampled (n = 100 neurons per iteration), and the mean ± bootstrapped s.e.m. of the average activity was plotted.

d.
Normalized decoding performance integrated over the indicated 1.5 s windows relevant for each task variable (mean ± bootstrapped s.e.m.; n = 100 iterations) as a function of day difference between the training of the classifier and testing in past and future days.Exponential decay functions were fit to estimate the decay in encoding stability across days (black lines denote fits to the average normalized performance).Note similar dynamics, with a faster decay in motor choice and post-decision outcome encoding, and a slower decay in context and post-trial outcome encoding.To obtain CIs for the decay constants, we also fit decay functions to each iteration of the decoder performance (95% CIs, context = 1.3 -13.1 days, motor = 0.6 -3.8 days, post-decision outcome = 2.8 -7.0 days, post-trial outcome = 6.4 -40.5 days).